Video Contents Understanding using Deep Neural Networks
This work addresses video content understanding for applications in adverse weather, but it is incremental as it builds on existing transfer learning and object detection techniques.
The paper tackles video classification by applying transfer learning to pre-weighted models, achieving superior performance over existing solutions like Google Video Intelligence API, with experiments conducted under foggy or rainy weather conditions.
We propose a novel application of Transfer Learning to classify video-frame sequences over multiple classes. This is a pre-weighted model that does not require to train a fresh CNN. This representation is achieved with the advent of "deep neural network" (DNN), which is being studied these days by many researchers. We utilize the classical approaches for video classification task using object detection techniques for comparison, such as "Google Video Intelligence API" and this study will run experiments as to how those architectures would perform in foggy or rainy weather conditions. Experimental evaluation on video collections shows that the new proposed classifier achieves superior performance over existing solutions.